The Challenge:

Dynamite Data was set up to solve a large, but important problem: To create the world's first central repository for e-commerce data. So-called "shopping engines" and price comparison sites actually rely on paid-placement programs, a model where merchants pay to have their information listed. The end user has no way of knowing if data from these engines is complete, timely, or accurate.


The Solution:

Dynamite Data solves this problem by using state-of-the-art datamining techniques to compile shopping data. Instead of requiring the merchant to provide data "feeds" for robots to parse, our robot identifies products and their street price organically using advanced pattern-matching techniques -- the same way a human brain does. As a result Dynamite Data can compile street prices, rebates and shipping information at the "buy page" level quickly and accurately.


How it works:

In many regards, Dynamite Data is a next-generation search engine. While search engines in the 1990's learned to perfect the art of data acquisition, computers are fast and sophisticated enough today to include logic that also allows them to interpret rudimentary data. This enables a new generation of search engines that don't just acquire data, but knowledge, as well.


Our Beginnings:

The premise for Dynamite Data began as research at the University of Illinois-Chicago and University of British Columbia in 2005. Our robot completed its first scan of the English-based consumer electronics online sector in 2007. By the end of 2008, Quarry Miner mapped more than 90% of all consumer electronics buy pages in North America, and nearly a third of all English-language retail Websites worldwide. Dynamite Data also made significant inroads in mapping non-English sites.


Building on the success of Quarry Miner, Dynamite Data offers novel and powerful ways to access data from its databases. Our company sells access to this data via application layers, dashboards, and provides free access to consumers via downloadable browser add-ons.





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